One-dimensional Convolutional Neural Network Model Research Articles

Non-invasive monitoring of microalgae cultivations using hyperspectral imager

AbstractHigh expectations are placed on microalgae as a sustainable source of valuable biomolecules. Robust methods to control microalgae cultivation processes are needed to enhance their efficiency and, thereafter, increase the profitability of microalgae-based products. To meet this need, a non-invasive monitoring method based on a hyperspectral imager was developed for laboratory scale and afterwards tested on industrial scale cultivations. In the laboratory experiments, reference data for microalgal biomass concentration was gathered to construct 1) a vegetation index-based linear regression model and 2) a one-dimensional convolutional neural network model to resolve microalgae biomass concentration from the spectral images. The two modelling approaches were compared. The mean absolute percentage error (MAPE) for the index-based model was 15–24%, with the standard deviation (SD) of 13-18 for the different species. MAPE for the convolutional neural network was 11–26% (SD = 10–22). Both models predicted the biomass well. The convolutional neural network could also classify the monocultures of green algae by species (accuracy of 97–99%). The index-based model was fast to construct and easy to interpret. The index-based monitoring was also tested in an industrial setup demonstrating a promising ability to retrieve microalgae-biomass-based signals in different cultivation systems.

The deep convolutional networks for the classification of multi-class arrhythmia

An arrhythmia is an irregular heartbeat. Many researchers in the AI field have carried out the automatic classification of arrhythmias, and the issue that has been widely discussed is imbalanced data. A popular technique for overcoming this problem is the synthetic minority oversampling technique (SMOTE) technique. In this paper, the author adds some sampling of data obtained from other datasets into the primary dataset. In this case, the main dataset is the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) arrhythmia database and an additional dataset from the MIT-BIH supraventricular arrhythmia database. The classification process is carried out with one-dimensional convolutional neural network model (1D-CNN) to perform multiclass and subject-class advancement of medical instrumentation (AAMII) classifications. The results obtained from this study are an accuracy of 99.10% for multiclass and 99.25% for subject-class.

Fault diagnosis and identification of rotating machinery based on one-dimensional convolutional neural network

The paper focuses on two kinds of rotating machinery, miniature table drilling machine and automobile engine, as the research object. Traditional machine learning has the need for manual feature extraction, and is very dependent on expert diagnostic experience and expertise, but also has the disadvantages of low accuracy, low timeliness, low efficiency, etc. For the traditional rotating machinery fault diagnosis method is more based on the traditional machine learning model, this paper puts forward a one-dimensional convolutional neural network-based fault identification method. According to the characteristics of the miniature table drilling machine and the automobile engine which are not detachable, the corresponding faults are set up respectively, Vibration signals of the attitude sensor are obtained by using the signal collector, and the collected data are preprocessed, then the CNN model is built for fault identification, and the network structure is constantly optimized to obtain the optimal network model with high accuracy (up to 100 %) and robustness. The results show that the one-dimensional convolutional neural network model improves the fault recognition accuracy and reduces the cost compared with the traditional machine learning SVM model when the original signal is used as the input signal.

Fault Diagnosis for Wind Turbine Flange Bolts Based on One-Dimensional Depthwise Separable Convolutions

In this paper, a new bolt fault diagnosis method is developed to solve the fault diagnosis problem of wind turbine flange bolts using one-dimensional depthwise separable convolutions. The main idea is to use a one-dimensional convolutional neural network model to classify and identify the acoustic vibration signals of bolts, which represent different bolt damage states. Through the method of knock test and modal simulation, it is concluded that the damage state of wind turbine flange bolt is related to the natural frequency distribution of acoustic vibration signal. It is found that the bolt damage state affects the modal shape of the structure, and then affects the natural frequency distribution of the bolt vibration signal. Therefore, the damage state can be identified by identifying the natural frequency distribution of the bolt acoustic vibration signal. In the present one-dimensional depth-detachable convolutional neural network model, the one-dimensional vector is first convolved into multiple channels, and then each channel is separately learned by depth-detachable convolution, which can effectively improve the feature quality and the effect of data classification. From the perspective of the realization mechanism of convolution operation, the depthwise separable convolution operation has fewer parameters and faster computing speed, making it easier to build lightweight models and deploy them to mobile devices.

Mental fatigue state recognition method based on convolution neural network and long short-term memory

The pace of modern life is accelerating, the pressure of life is gradually increasing, and the long-term accumulation of mental fatigue poses a threat to health. By analyzing physiological signals and parameters, this paper proposes a method that can identify the state of mental fatigue, which helps to maintain a healthy life. The method proposed in this paper is a new recognition method of psychological fatigue state of electrocardiogram signals based on convolutional neural network and long short-term memory. Firstly, the convolution layer of one-dimensional convolutional neural network model is used to extract local features, the key information is extracted through pooling layer, and some redundant data is removed. Then, the extracted features are used as input to the long short-term memory model to further fuse the ECG features. Finally, by integrating the key information through the full connection layer, the accurate recognition of mental fatigue state is successfully realized. The results show that compared with traditional machine learning algorithms, the proposed method significantly improves the accuracy of mental fatigue recognition to 96.3%, which provides a reliable basis for the early warning and evaluation of mental fatigue.

The Temperature Field Prediction and Estimation of Ti-Al Alloy Twin-Wire Plasma Arc Additive Manufacturing Using a One-Dimensional Convolution Neural Network

Plasma arc deposition as an additive manufacturing technology has unique advantages for producing parts with complex shapes through layer-by-layer deposition. It is critical to predict and control the temperature field during the production process due to the temperature distribution and gradients determining the properties and performance of the part. Numerical simulation approaches, such as the finite element method, which provides a large amount of data for machine learning modeling, thus reducing the overhead of experimental measurements, are widely used in machine learning. In this paper, we propose a neural network combined finite element method and process prediction workflow. A one-dimensional convolutional neural network model for predicting 2D temperature distribution is developed by training the collected data on the planar temperature field of titanium–aluminum twin-wire plasma arc additive manufacturing and the finite element method. The results show that the predicted temperature mean square error is only 0.5, with less than a 20 °C error in peak temperature and a relative error below 1%. The proposed transfer learning method achieves the same training loss and is 500 iterations faster than basic training, which improves the training speed by 25%. The current study confirms the accurate performance of the ML model and the effectiveness of the optimization method.

Research on high proportion of clean energy grid-connected oscillation risk prediction technology based on CNN and trend feature analysis

Oscillations, commonly known as a universal, propagative, and intricate event in the new power system, often give rise to generator tripping and load shedding, not only adversely affecting the power flow limit and the power angle stability but also posing threats to the lines of defense for stability and protection. Traditionally, emphasis has been laid on post-fault oscillation management, an emergency measure to deal with the impact and damage that have already affected the power grid. As such, this paper focuses on an oscillation prediction technique to detect oscillation energy early and intervene proactively to prevent further faults. This technique effectively lessens the damage caused by impacts and disconnects to the power grid. Firstly, this paper proposes the concept of disturbance power density and establishes the correlation between disturbance energy and the time domain, thereby exploring a method for evaluating the pattern of electrical quantities before power system oscillation. Secondly, it speeds up the time it takes to detect faults by catching nuances of voltage-current phase angle and impedance. Lastly, it puts forward a technique to cope with the intricacy and variety of power grid equipment using the convolutional neural network (CNN). This technique incorporates an integrated attention mechanism within a one-dimensional CNN model to capture the implicit mapping between voltage, active power, and reactive power at any time in the power system. This enables the model to self-learn multi-device characteristics and enhances the possibility of using theory in practical ways. Moreover, practical case studies also show that the prediction technique proposed in this paper can effectively issue warnings eight minutes before the occurrence of oscillation.

Temperature prediction of Bragg grating sensing based on a one-dimensional convolutional neural network.

In this paper, we propose a new (to us) way of demodulating the grating sensing spectrum using a one-dimensional convolutional neural network (1DCNN) to overcome the limitation of the traditional fitting method of temperature demodulation for subway tunnel fires. This method constructs a one-dimensional convolutional neural network model and combines it with the experimental device of a fiber Bragg grating (FBG) temperature measurement. One thousand eight hundred spectra of experimental data are selected as sample data for training. Adam's random optimization algorithm is used in training to predict the temperature of multiple periods, with an accuracy of 99.95% and a root-mean-square deviation (RMSE) of 0.0832°C. The experiment shows that the algorithm in this paper is better than the GRU and LSTM algorithms, as traditional maximum peak methods, and can effectively improve the measurement accuracy. This article aims to provide a high-speed demodulation solution for FBG-based sensing systems to meet the practical needs of large-scale real-time monitoring.

Fault diagnosis method of multi-rotor UAV based on one-dimensional convolutional neural network with adaptive batch normalization algorithm

In this paper, we propose a one-dimensional convolutional neural network model based on the adaptive batch normalization (AdaBN) algorithm to improve the CNN model, which is difficult to extract features from multi-rotor unmanned aerial vehicle (UAV) rotor structural faults under variable conditions and has poor fault diagnosis performance. The method accomplishes fault diagnosis and classification by feature extraction from lower dimensional multi-rotor UAV data. The AdaBN algorithm adjusts the parameters of the BN layer in the model during the testing phase to improve the domain adaptive capability of the model in scenarios with variable operating conditions. Also, to improve the robustness of the model under noisy conditions, the first layer of convolutional kernel dropout operation is introduced to improve the noise immunity of the model. To reduce the complexity of manual tuning and to find the optimal combination of hyperparameters for the network model more effectively, the grey wolf optimizer algorithm is used to optimize the hyperparameters and further improve the model performance. Finally, the effectiveness of the proposed method is verified through comparison tests, and it shows good diagnostic effects in noise and variable conditions compared with several commonly used fault diagnosis methods.

1-D convolutional neural network-based crosstalk quality assessment

Due to the current state of the declining field of crosstalk, many studies are engaged in investigating causes and devising strategies for its improvement. Nonetheless, there are often no quantitative measures available for assessing the quality of crosstalk. This study employed recordings made during traditional crosstalk performances to assess crosstalk quality based on the percentage of positive audience feedback sounds such as applause and laughter throughout the performance. This study compares Mel Frequency Cepstral Coefficients (MFCC) and Mel Filterbank Energies (MFE) audio feature extraction methods and compares different classification models by training a one-dimensional convolutional neural network model to explore the model that performs better in audio classification under low audio quality conditions, such as low sampling rate and small signal-to-noise ratio. In this study, the training data set has been divided into two different schemes. One is the sound of laughter, applause, singing and speaking, the other is the sound of speaking, singing and laughter&applause. In this study, the final performance of the different models, including accuracy and loss, is counted. The experimental results demonstrated that the models obtained when the MFE method is used and the audio classification is labeled as singing, applause and laughter, and performance speech training have better performance when the comic audio is classified.

A Methodology Based on 1D-CNN and Bootstrap Method to Estimate the Remaining Useful Life of Industrial Assets Suffering from Generalized Corrosion

The main goal of this work is to propose an efficient and accurate modern methodology to estimate the useful life of industrial assets used in oil and gas industry suffering from generalized corrosion. It merges the convolutional neural networks, extreme value theory, and bootstrap methods to handle the available corrosion data obtained through nondestructive inspection techniques for structural integrity assessments. It is due to the high cost of inspection techniques actually used in many industries to generate a reliable large amount of data to be analyzed by traditional statistical tools and technical factors, such as the inaccessibility of certain zones of the assets. First, the most appropriate extreme value distribution is determined to best fit the available inspection data, aiming to generate sufficient information for the training and testing processes of a one-dimensional convolutional neural network model to improve the accuracy of the useful life estimation. To demonstrate the main features and capabilities of the methodology, the dataset of AISI 1018 steel tubes of a heat exchanger used in a Brazilian refinery subjected to a general corrosion-type extreme process is retained herein. The results demonstrate that it is an interesting tool for inspection process to assist engineers and/or users in predictive maintenance phases to access the structural integrity of industrial assets subjected to extreme events such as general corrosion.

Dynamic displacement estimation of structures using one-dimensional convolutional neural network

For large infrastructures, dynamic displacement measurement in structures is an essential topic. However, limitations imposed by the installation location of the displacement sensor can lead to measurement difficulties. Accelerometers are characterized by easy installation, good stability and high sensitivity. For this regard, this paper proposes a structural dynamic displacement estimation method based on a one-dimensional convolutional neural network and acceleration data. It models the complex relationship between acceleration signals and dynamic displacement information. In order to verify the reliability of the proposed method, a finite element-based frame structure was created. Accelerations and displacements were collected for each node of the frame model under seismic response. Then, a dynamic displacement estimation dataset is constructed using the acceleration time series signal as features and the displacement signal at a certain moment as target. In addition, a typical neural network was used for a comparative study. The results indicated that the error of the neural network model in the dynamic displacement estimation task was 9.52 times higher than that of the one-dimensional convolutional neural network model. Meanwhile, the proposed modelling scheme has stronger noise immunity. In order to validate the utility of the proposed method, data from a real frame structure was collected. The test results showed that the proposed method has a mean square error of only 5.097 in the real dynamic displacement estimation task, which meets the engineering needs. Afterwards, the outputs of each layer in the dynamic displacement estimation model are visualized to emphasize the displacement calculation process of the convolutional neural network.

Bathymetry Inversion Using Attention-Based Band Optimization Model for Hyperspectral or Multispectral Satellite Imagery

Satellite-derived bathymetry enables the non-contact derivation of large-scale shallow water depths. Hyperspectral satellite images provide more information than multispectral satellite images, making them theoretically more effective and accurate for bathymetry inversion. This paper focuses on the use of hyperspectral satellite images (PRISMA) for bathymetry inversion and compares the retrieval capabilities of multispectral satellite images (Sentinel-2 and Landsat 9) in the southeastern waters of Molokai Island in the Hawaiian Archipelago and Yinyu Island in the Paracel Archipelago. This paper proposes an attention-based band optimization one-dimensional convolutional neural network model (ABO-CNN) to better utilize the increased spectral information from multispectral and hyperspectral images for bathymetry inversion, and this model is compared with a traditional empirical model (Stumpf model) and two deep learning models (feedforward neural network and one-dimensional convolutional neural network). The results indicate that the ABO-CNN model outperforms the above three models, and the root mean square errors of retrieved bathymetry using the PRISMA images are 1.43 m and 0.73 m in the above two study areas, respectively. In summary, this paper demonstrates that PRISMA hyperspectral imagery has superior bathymetry inversion capabilities compared to multispectral images (Sentinel-2 and Landsat 9), and the proposed deep learning model ABO-CNN is a promising candidate model for satellite-derived bathymetry using hyperspectral imagery. With the increasing availability of ICESat-2 bathymetric data, the use of a combination of the proposed ABO-CNN model and the ICEsat-2 data as the training data provides a practical approach for bathymetric retrieval applications.

Hyperspectral imaging to predict the effect of cyclophosphamide in primary membranous nephropathy

BackgroundPresently, there is a lack of accurate predictors of the efficacy of primary membranous nephropathy. The aim of this study is to explore the application value of hyperspectral imaging in predicting the efficacy of cyclophosphamide treatment in primary membranous nephropathy. MethodA total of 30 patients with primary membranous nephropathy who were treated with glucocorticoid combined with cyclophosphamide were collected. Hematoxylin-eosin stained renal pathological images were acquired by hyperspectral imaging system at the spectral range of 400–1000 nm. The remission group data set contained 23 samples, while the non-remission group data set contained 28 samples. A one-dimensional convolutional neural network model was established to train and test the hyperspectral data, and the performance of the model was evaluated. ResultFrom the spectral curve, the spectral difference between the remission group and the non-remission group was obvious between 525 and 700 nm. The spectral data in this band were analyzed by one-dimensional convolutional neural network, and the confusion matrix showed that the remission group and the non-remission group were successfully classified. The precision and recall were 0.89 and 0.81 for the non-response group and 0.83 and 0.90 for the response group, respectively, with an F1 score of 0.85 in both groups. The area under the AUC curve of the classification model reached 0.857. ConclusionIn this study, a one-dimensional convolutional neural network model was used to analyze the hyperspectral images of renal pathology of PMN patients, and the patients in remission group and non-remission group were successfully classified after glucocorticoid combined with cyclophosphamide treatment.

Spectroscopy identification method and system for coal and gangue based on multilocation feature fusion

ABSTRACT Visible and near-infrared (VIS-NIR) spectral technology is fast, accurate, and non-destructive, which meets the requirements of the separation of coal and gangue. However, the performance of the classification models established by the spectra collected according to the usual single-location collection methods was unsatisfactory. Simulating the production conditions of coal gangue sorting, this paper proposed a multilocation spectral feature fusion identification method for coal and gangue to improve the identification accuracy and developed an identification system. In the multilocation spectral collection method, the spectra of the sample were collected at three locations under the probe, which obtained richer information. In the classification models built based on different feature fusion methods, the one-dimensional convolutional neural network model with feature layer fusion had the strongest classification ability, with an accuracy of 97.61%, an improvement of 8.84% over the best single-location model. The regions of interest for model decision-making were visualized using the Gradient-weighted Class Activation Mapping (Grad-CAM) method to explore the reasons why the multilocation feature fusion method was more advanced. Furthermore, a coal gangue identification system was developed using LabVIEW, which can achieve fast and accurate recognition with an accuracy of 93.15% and an expenditure time of 1.241 s.

Convolutional neural network based on photoplethysmography signals for sleep apnea syndrome detection.

The current method of monitoring sleep disorders is complex, time-consuming, and uncomfortable, although it can provide scientifc guidance to ensure worldwide sleep quality. This study aims to seek a comfortable and convenient method for identifying sleep apnea syndrome. In this work, a one-dimensional convolutional neural network model was established. To classify this condition, the model was trained with the photoplethysmographic (PPG) signals of 20 healthy people and 39 sleep apnea syndrome (SAS) patients, and the influence of noise on the model was tested by anti-interference experiments. The results showed that the accuracy of the model for SAS classifcation exceeds 90%, and it has some antiinterference ability. This paper provides a SAS detection method based on PPG signals, which is helpful for portable wearable detection.

Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, Greece

The main scope of the study is to evaluate the prognostic accuracy of a one-dimensional convolutional neural network model (1D-CNN), in flood susceptibility assessment, in a selected test site on the island of Euboea, Greece. Logistic regression (LR), Naïve Bayes (NB), gradient boosting (GB), and a deep learning neural network (DLNN) model are the benchmark models used to compare their performance with that of a 1D-CNN model. Remote sensing (RS) techniques are used to collect the necessary flood related data, whereas thirteen flash-flood-related variables were used as predictive variables, such as elevation, slope, plan curvature, profile curvature, topographic wetness index, lithology, silt content, sand content, clay content, distance to faults, and distance to river network. The Weight of Evidence method was applied to calculate the correlation among the flood-related variables and to assign a weight value to each variable class. Regression analysis and multi-collinearity analysis were used to assess collinearity among the flood-related variables, whereas the Shapley Additive explanations method was used to rank the features by importance. The evaluation process involved estimating the predictive ability of all models via classification accuracy, sensitivity, specificity, and area under the success and predictive rate curves (AUC). The outcomes of the analysis confirmed that the 1D-CNN provided a higher accuracy (0.924), followed by LR (0.904) and DLNN (0.899). Overall, 1D-CNNs can be useful tools for analyzing flood susceptibility using remote sensing data, with high accuracy predictions.

A Convolutional Neural Network Model for Soil Temperature Prediction under Ordinary and Hot Weather Conditions: Comparison with a Multilayer Perceptron Model

Soil temperature is a critical parameter in soil science, agriculture, meteorology, hydrology, and water resources engineering, and its accurate and cost-effective determination and prediction are very important. Machine learning models are widely employed for surface, near-surface, and subsurface soil temperature predictions. The present study employed a properly designed one-dimensional convolutional neural network model to predict the hourly soil temperature at a subsurface depth of 0–7 cm. The annual input dataset for this model included eight hourly climatic features. The performance of this model was assessed using a wide range of evaluation metrics and compared to that of a multilayer perceptron model. A detailed sensitivity analysis was conducted on each feature to determine its importance in predicting the soil temperature. This analysis showed that air temperature had the greatest impact and surface thermal radiation had the least impact on soil temperature prediction. It was concluded that the one-dimensional convolutional model performed better than the multilayer perceptron model in predicting the soil temperature under both normal and hot weather conditions. The findings of this study demonstrated the capability of the model to predict the daily maximum soil temperature.

Bearing fault diagnosis under various conditions using an incremental learning-based multi-task shared classifier

Rolling bearings are susceptible to failure because of their complex and severe working environments. Deep learning-driven intelligent fault diagnosis methods have been widely introduced and exhibit satisfactory performance. However, bearing fault diagnosis during various working conditions is challenging; catastrophic forgetting occurs when test data are gathered under different conditions. In this paper, we develop an incremental learning-based multi-task shared classifier (IL-MTSC) for bearing fault diagnosis under various conditions. We use a one-dimensional convolutional neural network model as the principal framework. Then, we create a knowledge distillation method that allows the model to retain learned knowledge. Finally, we use a shared classifier that operates under various scenarios to establish a unified structure. The method can continuously learn and preserve knowledge when diagnoses bearing faults under various working conditions. The results show that IL-MTSC optimally diagnoses bearing marks under various conditions.

Emotion Recognition System Based on Two-Level Ensemble of Deep-Convolutional Neural Network Models

Emotions play a crucial role in human interaction and healthcare. This study introduces an automatic emotion recognition system based on deep learning using electroencephalogram signals. A lightweight pyramidal one-dimensional convolutional neural network model is proposed that involves a small number of learnable parameters. Using the model, a two-level ensemble classifier is designed. Each channel is scanned incrementally in the first level to generate predictions, which are fused using the majority vote. The second level fuses the predictions of all the channels of a signal using a majority vote to predict the emotional state. The method was validated using the public domain challenging benchmark dataset. The electroencephalogram signals over five brain regions were analyzed. The results indicate that the frontal brain region plays a dominant role in two emotion recognition problems (distinguishing high valence vs. low valence and high arousal vs. low arousal states).